Choosing the right AI model for your needs can be daunting. As SLMs and LLMs become increasingly sophisticated, selecting the correct deployment for your goals can be challenging. In this post, we'll break down the differences between SLMs and LLMs to help you choose the model that will maximize performance while minimizing costs and latency for the most efficient, scalable, and effective AI deployment. Multi Agent AI systems can also benefit from choosing the right model, especially when optimizing collaboration between agents.
One valuable resource to help you achieve your objectives is Lamatic's generative AI tech stack. The solution features a modular architecture that enables you to leverage SLMs and LLMs to complement each other and maximize performance for your specific use case.
What are LLMs and SLMs?
Large language models (LLMs) and small language models (SLMs) differ in size, capabilities, and performance. LLMs are powerful AI models trained on vast datasets to handle complex language tasks, while SLMs are optimized for efficiency and require fewer resources.
Does Size Matter?
In AI software development, the answer is "it depends." The novelty of large language models (LLMs) wore off as models' improvements slowed, reaching a plateau in 2024.
At the same time, a new trend has emerged, positioning small language models (SLMs) as a go-to option to reap the benefits of generative AI faster and without breaking the bank.
When Should Businesses Downsize to SLMs?
Value-oriented business owners picked up on the changes but faced a dilemma about LLM vs. SLM. When is it reasonable to downsize to an SLM, and when does banking on an LLM pay off?
What Defines LLMs vs. SLMs?
Categorization into small and large language models is determined by the number of parameters in their neural networks. We stick to Gartner's and Deloitte's vision as the definitions vary. While SLMs are models that fit the 500 million to 20 billion parameter range, LLMs hit the 20 billion mark.
How Do LLMs and SLMs Work?
Regardless of size, language models represent AI algorithms powered by deep learning, enabling them to excel at natural language understanding and processing tasks.
Under the hood, all transformer models consist of artificial neural networks, including:
- An encoder to grasp the human language input
- A decoder to generate a contextually appropriate output
What’s the Deal with Generative AI?
When you use generative AI, such as the widely popular ChatGPT, you use an underlying capability called a large language model. This consists of a computational and mathematical model that has been data-trained on a large amount of human writing.
The Internet is scanned for all human-written content, such as essays, narratives, poems, and the like, which are then used for extensive pattern-matching. The aim is for AI to computationally mimic how humans compose sentences and make use of words.
What Are the Practical Differences Between LLMs and SLMs?
Sometimes, nice things come in small packages. SLMs are like mini-versions of LLMs. They do not require an Internet connection and are shaped to hopefully work suitably on small standalone devices.
Are LLMs and SLMs Opponents?
If you’ve not heard about small language models, that’s perfectly understandable since they are still not entirely up to par. There is a wide variety of experimental SLMs; some are good while others are clunky and less appealing. To clarify, I’m not suggesting that there aren’t valid SLMs that can do a decent job for you right now.
It's just that, on a widespread basis, we are still in the infancy or early days of robust SLMs. The breakout of SLMs into the mainstream marketplace is yet to come. Mark my words, the day of the SLM is coming. Their glory will be had.
Debunking the LLM vs. SLM Debate
Some critics get themselves into a tizzy and believe that you either must favor LLMs or you must favor SLMs. They want to divide or polarize people into one of two camps.
You either love the largeness of LLMs and detest the smallness of SLMs, or you relish the compactness of SLMs and outright hate the oversized nature of LLMs. They seek to trap you into a mindset you must somehow choose between.
What Are the Trade-Offs Between LLMs and SLMs?
There are great uses of LLMs, as well as great uses of SLMs. Do not let yourself be pushed into a boxed posture that one path is terrible and the other is good. It’s just nonsense to make a broad generalization like that. Each approach has its advantages and disadvantages. I compare this to cars.
Sometimes, a large, powerful car is the best choice for your needs. Maybe you are driving across the country with your entire family. In other instances, a compact car is a better choice, such as making quick trips around town by yourself and wanting to squeeze in and out of traffic.
Embracing Both LLMs and SLMs
You must consider various crucial factors, such as cost, speed, comfort, and so on, to make a sensible and reasonable decision. My viewpoint, which is somewhat contrarian for those bitter critics, is that we should actively and avidly pursue both avenues, namely LLMs and SLMs, with equal vigor.
Do not drop one for the other. Keep making progress in both directions. We can do this simultaneously and do not have to favor one path only. Yay, let’s get larger with LLMs. And, yay, let’s get smaller with SLMs. LLMs and SLMs are pals, not opponents.
Example Of The Tradeoffs Afoot
You might be curious about the tradeoffs between the largeness of LLMs and the smallness of SLMs. Answering that question is a slippery slope. I state this because just as LLMs are getting better via AI advances, the same is true for SLMs.
Thus, any example of what LLM or SLM does or doesn’t do right now is rife for ridicule in a few years or even months as to progress being made. I compare this to the advances made in smartphones. Think back to the release of the first iPhone models. At the time, they were considered quite advanced.
A Present-Day Comparison
If you compare that initial model to the latest version of the iPhone, you will laugh aloud at how puny or limited the first iPhone was. It had no internal memory, less capable cameras, screen size and density drawbacks, and other elements that seem absurd to us now. But at the time, we were overjoyed at its capabilities. Please keep that lesson in mind.
Okay, with those noted caveats, I will give you an example showcasing the difference between an SLM and an LLM. First, let’s try out a contemporary LLM and see what we get as an answer to a straightforward question.
I entered the prompt into LLM:"Can you explain the theory of relativity and how it changed our understanding of space and time?"
LLM generated response:"The theory of relativity, developed by Albert Einstein, fundamentally changed our understanding of space, time, and gravity. There are two main parts: special relativity and general relativity.
Special relativity, published in 1905, introduced the concept that space and time are interconnected and form a single continuum called spacetime. It posits that the speed of light is constant for all observers, regardless of their motion, leading to phenomena like time dilation, where time slows down for objects moving close to the speed of light, and length contraction, where objects appear shorter in the direction of motion.
General relativity, published in 1915, expanded these ideas by introducing gravity as the curvature of spacetime caused by massive objects. This replaced the Newtonian view of gravity as a force acting at a distance. According to general relativity, objects like planets and stars warp spacetime, and this curvature is what we experience as gravity.”
Take a look at the answer. The response generated by the LLM is reasonably valid and provides an illuminating and somewhat detailed answer.
What Can An SLM Do?
I will use the same question with a typical SLM nowadays.
I entered the prompt into SLM:"Can you explain the theory of relativity and how it changed our understanding of space and time?"
SLM generated response:"The theory of relativity, developed by Albert Einstein, changed how we understand space, time, and gravity. Special relativity says that the speed of light is always the same, no matter how fast someone is moving. This means that when objects move near the speed of light, time slows down for them (time dilation), and they appear shorter (length contraction). General relativity describes gravity differently from before. Instead of a force, it’s the bending of space and time around large objects, like planets.”
Key Differences
Go ahead and look at the answer from the SLM. Do you see any differences between the LLM-generated response and the SLM-generated response?
A typical difference is that SLMs tend to have fewer details in their internal structures. Since they aren’t usually actively connected to the web, they don’t conventionally look up additional info (exceptions apply, as I’ll note momentarily).
Depth and Timeliness
The response by the SLM about the theory of relativity is a bit shallower than the response produced by the LLM. It might also be less timely in terms of whatever the latest online commentary on whatever topic you are asking about.
Do not, though, take that to the bank. Some SLMs are specifically focused on particular domains or topics; therefore, they can potentially outdo a large generic LLM with online access.
Standalone or Online?
Also, some SLMs allow you to tell the AI to go ahead and access the Internet, which seems odd. Isn’t the beauty of the SLM that it can be standalone? Yes, that’s true. At the same time, nothing prevents an AI maker from letting you decide to allow online access.
If you grant that access, the particular SLM can seek an Internet connection to find more data about the matter at hand.
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Complete SLM vs LLM Guide for Efficient and Scalable AI
SLMs and LLMs operate differently. Given their vastness, LLMs tend to have a wide range of language mimicry. They can be impressive in what data they have and can discuss, while SLMs typically are compacted, having less data to rely on. This becomes quickly evident after extensive usage of both.
Meanwhile, performance on large servers in the cloud for LLMs makes them quick to respond, though you are competing with millions of other users. An SLM relies solely on the memory and processing speed of your handheld smart device or laptop.
Accuracy vs. Adaptability: Which One Gets It Right?
LLMs are suitable for open-ended questions, varied problems, and complex tasks. SLMs, on the other hand, are best used if the questions are narrowly within whatever data training the SLM was devised on.
Usually, you would ask SLMs more straightforward questions and reserve more challenging questions for LLMs. The exception is that if the SLM is tailored to a niche, it might be better than the LLM at answering those more complex questions related to the niche.
Cost vs. Accessibility: Which Model Is More Cost-Effective?
LLMs are getting better, partially by making them larger and larger, which requires expensive computational processing resources. SLMs are less costly since they rely on the device's processing capabilities.
Since an SLM is usually designed to work primarily on a standalone basis, you also avoid the Internet connection costs that arise when using LLMs. The lower price comes with likely narrower capabilities.
Latency vs. Depth of Interaction: Do You Want Speed or Depth?
The response time can be blazingly fast if an SLM is well-devised to run on particular standalone devices. The compactness, customization, and the lack of needing to rely on an online connection make this possible.
But if the SLM is not well-designed or runs willy-nilly, the local device might not have enough horsepower, and the wait times could be exasperating. LLMs have the advantage of relying on souped-up servers, though you are competing with millions of others using the same LLM.
User Privacy vs. Cloud Dependency: Which One Is More Secure?
LLMs that primarily work in the cloud expose you to potential privacy concerns since your entered prompts flow into the cloud. Theoretically, SLMs keep your data local to the device and won’t release it.
I say in theory because some SLM makers do nonetheless store your local data and reserve the right to move it up into their cloud for reasons like improving their SLMs. Don’t assume that just because you are using an SLM, it is somehow a privacy-saving approach. It might be, it might not be.
LLMs vs. SLMs: Different Data Sets, Different Outputs
LLMs and SLMs are usually trained on different data sets. The scope of GPT-4/ChatGPT is an excellent example demonstrating one common difference: the data sets they’re trained on.
LLMs are usually intended to emulate human intelligence at a comprehensive level and thus are trained on a wide range of large data sets. In the case of GPT-4/ChatGPT, that includes the entire public internet(!) up to a specific date. This is how ChatGPT has gained notoriety for interpreting and responding to such a wide range of queries from general users.
LLM Hallucinations vs. SLM Precision
This is also why it has sometimes gained attention for potentially incorrect responses, colloquially referred to as “hallucinations.” It lacks the fine-tuning and domain-specific training to respond accurately to every industry-specific or niche query.
SLMs, on the other hand, are typically trained on smaller data sets tailored to specific industry domains (i.e., areas of expertise).
Domain-Specific Strengths
For example, a healthcare provider could use an SLM-powered chatbot trained on medical data sets to inject domain-specific knowledge into a user’s non-expert query about their health, enriching the quality of the question and response.
In this case, the SLM-powered chatbot doesn’t need to be trained on the entire internet—every blog post, fictional novel, or poem ever written—because it’s irrelevant to the healthcare use case. In short, SLMs typically excel in specific domains but struggle compared to LLMs in general knowledge and overall contextual understanding.
LLMs vs. SLMs: Different Training Processes
The size and scope of data sets aren’t the only factors that differentiate SLMs from LLMs. A model can be considered an SLM even if trained on the same data sets as an LLM.
That’s because the training parameters and overall process—not just the amount of data—are part of defining each model. In other words, what’s important isn’t just how much data a model is trained on and what it is designed to learn from that data.
Parameters
In machine learning, parameters are internal variables that determine a model's predictions. In other words, parameters are how models decide what to do with the data set's raw material.
During training, an AI model continuously adjusts its parameters to improve predictions—think of it like turning a knob on a radio to find the right channel. Beyond the total number of parameters, other factors in this immensely complicated process include:
- How parameters are layered into a model
- How they’re weighted against each other
- How they’re optimized for pattern recognition versus simple memorization
No industry definition for how many parameters equate to an SLM versus an LLM. What’s most relevant is that SLMs typically contain far fewer parameters than LLMs because their use cases are more focused on specific knowledge domains.
How They Compare to SLMs
The LLM GPT-4/ChatGPT was purportedly trained on trillions of parameters to respond to almost any user input. It’s worth noting that GPT-4 is a uniquely prominent example of an LLM.
There are many examples of smaller LLMs (not quite SLMs), like IBM’s open-source Granite models, which range in size from 3 to 35 billion parameters. SLMs typically boast fewer parameters (sometimes still ranging in the billions) because the expected applications are much narrower.
Fine-tuning
Fine-tuning, another aspect of model training that can differentiate SLMs and LLMs, involves adapting and updating a pretrained model with new data. It typically consists in customizing a pretrained model to a specific use case.
This involves introducing new data sets to test whether the existing parameters can produce acceptable results in a new context. In general, fine-tuning is more brutal, takes more time, and is more resource-intensive the more parameters a model contains, meaning LLMs require a heavier lift than SLMs.
Training Differences
Beyond parameters and fine-tuning, the type and complexity of the training process are also usually different between SLMs and LLMs. Understanding different types of model training, like “self-attention mechanisms” or “encoder-decoder model schemes,” requires a high level of data science expertise.
The fundamental differences between SLM and LLM training are that SLMs usually favor more resource-efficient approaches and focus on specific use cases than their LLM counterparts.
Bias
Although every AI model undergoes some degree of fine-tuning, the scope of most LLMs makes it impossible to tune them to every possible inference. LLMs are also typically trained on openly accessible data sets like the Internet, whereas SLMs often train on industry—or company-specific data sets.
Mitigating Bias in LLMs vs. SLMs
This can introduce biases, such as underrepresenting or misrepresenting certain groups and ideas, or factual inaccuracies. Because LLMs and SLMs are language models, they can also inherit language biases related to dialect, geographical location, and grammar.
In short, any language model can inherit bias, but LLMs in particular, given their scope, introduce more opportunities for bias. With SLMs trained on smaller data sets, you can more easily mitigate the biases that will inevitably occur.
Resource Requirements for LLMs vs. SLMs
Training any model for a business use case, whether LLM or SLM, is a resource-intensive process. Training LLMs is incredibly resource-intensive. In the case of GPT-4, 25,000 NVIDIA A100 GPUs ran simultaneously and continuously for 90-100 days.
Again, GPT-4 represents the most significant end of the LLM spectrum. Other LLMs like Granite didn’t require as many resources. Training an SLM still likely requires substantial compute resources, but far fewer than an LLM requires.
Resource Requirements for Training vs. Inference
It’s also important to note the difference between model training and inference. As discussed above, training is the first step in developing an AI model. Inference is the process a trained AI model follows to predict new data.
For example, when a user asks ChatGPT a question, that invokes ChatGPT to return a prediction to the user—that process of generating a prediction is an inference.
Inference Efficiency
Some pretrained LLMs, like the Granite family of models, can make inferences using the resources of a single high-power workstation (e.g., Granite models can fit on one V100-32GB GPU2). Many require multiple parallel processing units to generate data.
Furthermore, the more concurrent users accessing an LLM, the slower the model runs inferences. SLMs, on the other hand, are usually designed to make inferences with the resources of a smartphone or other mobile device.
Cost/Benefits of Getting Started with LLMs vs. SLMs
There’s no answer to the question “which model is better?” Instead, it depends on your organization’s plans, resources, expertise, timetable, and other factors.
It’s also important to decide whether your use case necessitates training a model from scratch or fine-tuning a pretrained model. Common considerations between LLMs and SLMs include:
Cost
LLMs generally require far more resources to train, fine-tune, and run inferences. Importantly, training is a less frequent investment. Computing resources are only needed while a model is being trained, which is an intermittent and not continuous task. Running inferences represents an ongoing cost, and the need can increase as the model's use is scaled to more users.
This usually requires large-scale cloud computing resources, a significant on-premise resource investment, or both. SLMs are frequently evaluated for low-latency use cases, like edge computing. That’s because they can often run with just the resources on a single mobile device without needing a constant, strong connection to more significant resources.
Expertise
Many popular pre-trained LLMs, like Granite, Llama, and GPT-4, offer a more “plug-and-play” option for getting started with AI. These are often preferable for organizations looking to begin experimenting with AI since they don’t need to be designed and trained from scratch by data scientists.
SLMs, on the other hand, typically require specialized expertise in data science and industry knowledge domains to fine-tune on niche data sets accurately.
Security
While the cost and quality of generative AI solutions have companies scratching their heads, security concerns top the list of hurdles. Both LLMs and SLMs present challenges, making businesses wary of investing.
When using language models, what can companies do to fortify their sensitive data, internal knowledge bases, and corporate systems? We suggest putting a premium on security best practices, including but not limited to:
- Data encryption to keep your sensitive information unreadable even if accessed by outside users
- Robust API to eliminate the risk of data interception
- Access control to ensure the model’s availability only for registered users
To implement these practices and create a solid language model usage policy, you may need the support of a gen AI-literate tech partner.
Output Quality: Why Getting Fast Answers Isn’t Enough
Speed isn’t the only parameter that matters when measuring language model performance. Besides getting answers quickly, you expect them to be accurate and relevant. That’s where the model’s context window, or context length, comes into play. It identifies the maximum amount of information within the ongoing conversation that a model can consider to generate a response.
A simple example is a summarization task, where your input will likely be significant. The larger the context window, the bigger the files you can summarize. Let’s say you want to elevate personalization in customer-facing services and decide to build a virtual financial advisor, as one of our clients did. You’ll need an LLM capable of considering previous conversations when answering new queries, at least ChatGPT 4.0 with 32K tokens of context length.
Context Window and Performance
A context window also influences the accuracy of a model’s answers when refining an initial request. Models can’t reach parts of the conversation outside their context length. With a larger window, you have more attempts to clarify the first input and get a contextually relevant answer.
Regarding model performance, LLMs beat SLMs. For example, GPT-4 Turbo has 128K tokens (around 240 document pages), and Claude 3 can cover a mind-boggling 200K tokens with remarkable accuracy. Meanwhile, the average context length of SLMs is about two to eight thousand tokens. For instance, Falcon 7B has 2K, while Mistral 7B and LLama 2 have 8K tokens.
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